Towards multiplexed in vivo phenotypic screening to improve antimicrobial drug discovery

Antimicrobial-resistant infections are prevalent worldwide, with drug-resistant strains emerging for all currently available antimicrobial drugs and the burden of disease estimated to be ~50,000 deaths/year in Europe and North America. Resistant microbes include bacteria such as Pseudomonas aeruginosa, and eukaryotes such Toxoplasma gondii, the causative agent of toxoplasmosis and a protozoan parasite for which resistance to front-line drugs is a growing concern. New therapeutic targets and drugs to treat these infections are urgently needed. Unfortunately, drug-discovery research and development (R&D) is costly in terms of time, money, and animal lives, with estimates of the cost of bringing a drug to market being $1-2.5 billion USD. Improvements in drug-discovery efficiency and success would reduce both cost and the number of animals used. Drug-discovery pipelines suffer from high failure rates, in part due to an inability to search for new drugs in vivo. The lack of knowledge of pathogen gene products essential for growth in vivo represents a significant gap in our knowledge. This limits our understanding and ability to develop drugs that can efficiently target pathogens in the context of an infection. These genes are an underutilized resource in the search for novel therapeutic targets. The ability to study host-pathogen interactions in vivo represents an exciting and scientifically important opportunity for the discovery of new biology, novel drugs and drug targets. It is a final frontier of host-interaction biology that remains inaccessible to many of the powerful high-throughput approaches shown to be successful in the past due to the experimentally intractable nature of the in vivo environment.

A Novel Solution: We hypothesise that biological evolutionary principles (natural selection, inheritance and mutation) can be harnessed to template the chemical evolution of novel antimicrobial drugs. This project will establish a new drug discovery approach: biology-templated synthesis (BTS), harnessing biology to guide discovery and evolution of novel antimicrobials. For BTS evolutionary principles (natural selection, inheritance and mutation) are translated into a synthetic chemistry strategy. Pathogenic microorganisms will template the chemical evolution of new antimicrobials. Initially applied to Toxoplasma for proof-of-concept, BTS will be used to discover modulators of parasite fitness and virulence within the intact murine host organism. In BTS a cell line of interest is molecularly barcoded with a DNA tag. A library of barcoded cells is generated and treated with a compound library. The barcode acts as a genetic tracer of the chemical treatment. The fitness of a barcoded cell line within a complex population can be mapped to an individual chemical treatment by deep sequencing of the molecular barcode. For barcoded pathogens, lead compound selection occurs at a population level within the complex environment of the host organism and the context of an infection. This allows the natural population-based infection process to select lead molecules. Least fit barcoded strains map to efficacious antimicrobial fragments. These fragments are inherited by the next generation of compounds, with diversity-oriented synthesis used as an organic chemistry surrogate for mutation, introducing unbiased appendage diversity onto the inherited fragment to produce the second-generation compound library. The screen is repeated, with potent compounds identified by natural selection in the host organism. This process is repeated iteratively. It will enable screening in vivo for the first time, and the application of evolutionary principles (natural selection, mutation, inheritance) to antimicrobial discovery. The advancement of multiplexed in vivo phenotypic screening will meaningfully impact upon animal use in drug discovery, simultaneously reducing numbers needed, improving study reproducibility, and reducing drug-pipeline failure rates.

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PhD Studentship


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Principal investigator

Dr Matthew Child


Imperial College London


Professor Jake Baum

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Award date:

Jan 2019 - Dec 2021

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